Adherence to treatment guidelines has been associated with improved long term clinical outcomes in adolescent diabetes. Despite the development of several probably efficacious interventions designed to improve self-management they have not been widely disseminated because of issues in feasibility. In order to address limitations of the traditional implementation of interventions, an internet-based problem solving intervention for adolescents with Type 1 diabetes is proposed. Aims include development of an intervention that may be administered through a website, determining feasibility, and conducting a small randomized trial of the intervention. The present approach to behavior change acknowledges that self-management is not viewed as the primary goal of the adolescent. Instead, the intervention seeks to link self-management to an individual's goals. Problem solving skills will be taught using multiple challenge-based learning cycles that each focus on a different barrier to self-management faced by adolescents. While teaching skills that have been reliably related to improved self-management, the intervention is considered exploratory as it translates an applied model and digital learning template from education for a chronic illness population and incorporates developmentally appropriate motivational theory. Patients will have access to a diabetes professional by posting questions to the website. Most clinical interactions involve didactic communication of what adolescents need to do, this intervention will help young patients determine why and how to implement what is recommended by their diabetes professional. If successful, the intervention will increase knowledge, problem solving skills, self-management behaviors, and ultimately clinical outcomes, using a cost-effective and sustainable methodology. Data from this research will be used to obtain further funding to test the intervention with a larger sample. A larger study will allow for reliable estimates of efficacy for clinical outcomes such as HbA1c, and will allow subgroup analyses with ethnicity, gender, and SES. [unreadable] [unreadable] [unreadable]